Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias-Variance Analysis

8Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Bagging can be interpreted as an approximation of random aggregating, an ideal ensemble method by which base learners are trained using data sets randomly drawn according to an unknown probability distribution. An approximate realization of random aggregating can be obtained through subsampled bagging, when large training sets are available. In this paper we perform an experimental bias-variance analysis of bagged and random aggregated ensembles of Support Vector Machines, in order to quantitatively evaluate their theoretical variance reduction properties. Experimental results with small samples show that random aggregating, implemented through subsampled bagging, reduces the variance component of the error by about 90%, while bagging, as expected, achieves a lower reduction. Bias-variance analysis explains also why ensemble methods based on subsampling techniques can be successfully applied to large data mining problems. © Springer-Verlag 2004.

Cite

CITATION STYLE

APA

Valentini, G. (2004). Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias-Variance Analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3077, 263–272. https://doi.org/10.1007/978-3-540-25966-4_26

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free